Vitals such as blood pressure, heart rate, respiration rate etc. are regularly monitored in hospitals to evaluate the physiological condition of a patient. The levels and trends in these signals can provide a wealth of information for disease progression, effects of drugs, the state of various organs, system problems, and the like. Such vitals are usually temporally successive measurements. Methods for accurately modeling patient physiological signals are increasingly needed to support clinical decisions and provide predictive tools.
To model physiological signals, one has to address the challenges of modeling the underlying dynamics of physiological systems that are not well modeled. Further, most predictive analytics tools use physiological signals to build their models. These measurements are typically not obtained at exact periodic intervals. At a given time-point, not all vitals of a patient may be measured, resulting in a number of missing values in the physiological data. Measurement data can be missing due to errors in recording, errors in the measurement devices, etc. Model-based methods which can forecast and impute missing patient vital measurement are increasingly needed for use in ICU Admission Prediction Systems which help identify patients requiring ICU admission, and in Emerging Complications Prediction Systems which help identify patients at risk for developing complications during their hospital stay. The present invention is specifically directed to forecasting and imputing patient vital measurements for healthcare analytics.